An important part of the comparison between the neural network and SVfit is the computation time. The benchmark SVfit algorithm requires 6 seconds of computation time for one event in our computer architecture. Our neural network algorithm requires 110 microseconds per event, a factor of fifty thousand times faster than the SVfit algorithm in the same architecture. The neural network algorithm, however, requires about 17 hours to train on 270'000 events with 400 epochs. But once the neural network is trained, the model architecture and the model weights can be saved and used later to make new predictions. In a different computing architecture, using a GPU accompanied by Tensorflow [1]} as the backend, the computation time to train the neural network was reduced down to 1.4 hours, with a prediction time of 7 microseconds per event.
